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Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. The library includes implementations of several stochastic local search algorithms, including simulated annealing, hill climbers, as well as constructive search algorithms such as stochastic sampling. Chips-n-Salsa now also includes genetic algorithms as well as evolutionary algorithms more generally. The library very extensively supports simulated annealing. It includes several classes for representing solutions to a variety of optimization problems. For example, the library includes a BitVector class that implements vectors of bits, as well as classes for representing solutions to problems where we are searching for an optimal vector of integers or reals. For each of the built-in representations, the library provides the most common mutation operators for generating random neighbors of candidate solutions, as well as common crossover operators for use with evolutionary algorithms. Additionally, the library provides extensive support for permutation optimization problems, including implementations of many different mutation operators for permutations, and utilizing the efficiently implemented Permutation class of the JavaPermutationTools (JPT) library. Chips-n-Salsa is customizable, making extensive use of Java's generic types, enabling using the library to optimize other types of representations beyond what is provided in the library. It is hybridizable, providing support for integrating multiple forms of local search (e.g., using a hill climber on a solution generated by simulated annealing), creating hybrid mutation operators (e.g., local search using multiple mutation operators), as well as support for running more than one type of search for the same problem concurrently using multiple threads as a form of algorithm portfolio. Chips-n-Salsa is iterative, with support for multistart metaheuristics, including implementations of several restart schedules for varying the run lengths across the restarts. It also supports parallel execution of multiple instances of the same, or different, stochastic local search algorithms for an instance of a problem to accelerate the search process. The library supports self-adaptive search in a variety of ways, such as including implementations of adaptive annealing schedules for simulated annealing, such as the Modified Lam schedule, implementations of the simpler annealing schedules but which self-tune the initial temperature and other parameters, and restart schedules that adapt to run length.

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/*
 * Chips-n-Salsa: A library of parallel self-adaptive local search algorithms.
 * Copyright (C) 2002-2021  Vincent A. Cicirello
 *
 * This file is part of Chips-n-Salsa (https://chips-n-salsa.cicirello.org/).
 *
 * Chips-n-Salsa is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Chips-n-Salsa is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see .
 */

package org.cicirello.search.operators;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import org.cicirello.math.rand.RandomIndexer;

/**
 * A WeightedHybridCrossover enables using multiple crossover operators, such that each time the
 * {@link #cross} method is called, a randomly chosen crossover operator is applied to the candidate
 * solutions. The random choice of crossover operator is weighted proportionately based on an array
 * of weights passed upon construction.
 *
 * Consider the following weights: w = [ 1, 2, 3]. In this example, the first crossover operator
 * will be used with probability 0.167, the second crossover operator will be used with probability
 * 2/6 = 0.333, and the third crossover operator will be used with probability 3/6 = 0.5.
 *
 * @param  The type of object used to represent candidate solutions to the problem.
 * @author Vincent A. Cicirello, https://www.cicirello.org/
 */
public final class WeightedHybridCrossover implements CrossoverOperator {

  private final ArrayList> ops;
  private final int[] choice;

  /**
   * Constructs a WeightedHybridCrossover from a Collection of CrossoverOperators.
   *
   * @param ops A Collection of CrossoverOperators.
   * @param weights The array of weights, whose length must be equal to ops.size(). Every element of
   *     weights must be greater than 0.
   * @throws IllegalArgumentException if ops doesn't contain any CrossoverOperators.
   * @throws IllegalArgumentException if ops.size() is not equal to weights.length.
   * @throws IllegalArgumentException if any weights are non-positive.
   */
  public WeightedHybridCrossover(Collection> ops, int[] weights) {
    if (ops.size() == 0)
      throw new IllegalArgumentException("Must pass at least 1 CrossoverOperator.");
    if (ops.size() != weights.length)
      throw new IllegalArgumentException(
          "Number of weights must be same as number of crossover operators.");
    choice = weights.clone();
    if (choice[0] <= 0) throw new IllegalArgumentException("The weights must be positive.");
    for (int i = 1; i < choice.length; i++) {
      if (choice[i] <= 0) throw new IllegalArgumentException("The weights must be positive.");
      choice[i] = choice[i - 1] + choice[i];
    }
    this.ops = new ArrayList>(ops.size());
    for (CrossoverOperator op : ops) {
      this.ops.add(op);
    }
  }

  /*
   * private constructor to support split method
   */
  private WeightedHybridCrossover(WeightedHybridCrossover other) {
    ops = new ArrayList>(other.ops.size());
    for (CrossoverOperator op : other.ops) {
      ops.add(op.split());
    }
    choice = other.choice.clone();
  }

  @Override
  public void cross(T c1, T c2) {
    int value = RandomIndexer.nextInt(choice[choice.length - 1]);
    int i = Arrays.binarySearch(choice, value);
    if (i < 0) i = -(i + 1);
    else i++;
    ops.get(i).cross(c1, c2);
  }

  @Override
  public WeightedHybridCrossover split() {
    return new WeightedHybridCrossover(this);
  }
}